Fuzzy Production System Research Articles

Особенности предоставления финансовой поддержки субъектам малого бизнеса рыбной отрасли с использованием продукционной модели базы знаний

The article highlights the procedure of selecting applications for state financial support for small businesses in the fishing industry. Criteria for expert evaluation of applications have been defined taking into account industry specifics and including the following blocks: “Compliance with formal requirements”, “Type of activity”, “Justification of the requested funding”, “Socio-economic contribution to the region”. The principles of developing the evaluation criteria are generalized. The examination procedure algorithm taking into account the observance of formal requirements at the first stage of the expertise has been worked out. The algorithm provides for the functionality of clarifying the type of activity and conducting assessment according to the relevant criteria. Aggregated indicators of expert assessments reduced to a linguistic description “high”, “medium”, “low” are obtained. The system of trapezoidal membership functions on the 01-carrier (five-factor 01-classifier) is illustrated. The applicability of fuzzy logic tools for the tasks of multi-criteria assessment and management decision-making is substantiated. A base of rules for fuzzy production has been developed, consisting of 9 rules and a set of management decisions on financial support. The implementation of the decision-making procedure as a fuzzy production model built on using FuzzyLogicToolbox is shown. The conclusion about the possibility of using the results in the design of intelligent decision support systems in the field of financing small and medium-sized businesses in the fishing industry is made. It has been stated that the fuzzy production system and the expert evaluation algorithm can be projected onto other branches of the national economy as a result of the configuration of the constituent elements.

Models and Methods of Information Support in the Management of the Maintenance and Repair of Equipment Based on Artificial Intelligence Technologies

Intelligent Information Support for Decision Making in Maintenance and Equipment Repair Management is discussed in this article. The authors propose a solution approach that includes Bayes method, knowledge engineering technologies and fuzzy logic to formalization of revealed and expert knowledge (production rules system) for diagnostics, (fuzzy production system) for classify equipment and components, forecasting and optimization methods for planning the components procurement. Practical realization is performed using analytical MATLAB platform and EXSYS Corvid and software developed in USATU. The proposed approach by authors of this article can be implemented as the local application for information system, or as part of the creation of the digital twin of the production system. The results of experimental studies have shown a significant gain in time in decision-making in the diagnostic task, as well as in the level of timely replacement of components by 12 percent due to the introduction of demand coefficients in planning purchases.

Production model for administration of judicial decisions in the case of theft

The economic essence of the theft, as a crime against property, and its connection to unemployment is revealed. The general model of the support system making court decisions as fuzzy production system is developed. For the case of theft (Article 185 of the Criminal Code of Ukraine), two variants of the implementation of the fuzzy production system - the Mamdani and Sugeno algorithms - are proposed. Incorporation of the developed model into the “Electronic Court” system, which is a feature of the information society, is able to increase the level of automation of judicial practice and prevent corruption.

Methods and algorithms of the spelling control and correction on the basis of fuzzy qualifier with MIMO-structure

The paper shows results of developing the conceptual principles and methods in construction of hyper semantic net for natural languages spelling control and correction on a basis of neural networks and methods of fuzzy logic at the expense of expert knowledge and account of uncertainty. The ways of formalization are offered for linguistic variable and parameters, represented quantitatively and qualitative, and for performance of fuzzy sets membership functions. The operability of net including various nodes of graph model is tested. The realization of net is carried out on the basis of model with MIMO-structure, combining in itself property of the neuro-fuzzy qualifier and fuzzy production system.

Tactile Recognition Based on Repetition of Grasping of an Object with a Robot Hand. Calculation Method for Increasing Probability Levels Using Fuzzy Relations.

A new robotic tactile recognition method wherein repetitions of grasping of an object enhanced recognition certainty was presented. To evaluate the certainty, probability levels of recognition were calculated by a fuzzy relational matrix defining similarities between objects. Calculations using various kinds of fuzzy compositions and binary operations required for the iterative evaluation process were compared. Arithmetic sum and product were found to be most suitable, being able to reflect the increase in probability levels. By applying the arithmetic calculations, a new program module for increasing probability levels was implemented. The module was incorporated into the fuzzy production system which was composed of a calculation module for characteristic parameters and an inference module using fuzzy production rules. Three modules were cyclically processed through repetitions of grasping of an object. A series of tactile recognition experiments using a robot hand equipped with three-axis tactile sensors were performed. Consequently, the present system succeeded in recognizing three-dimensional shapes of unknown objects such as an icosahedron and an imperfect sphere, which could not be recognized through only a single grasping.

Hybrid Connectionist Fuzzy Production System: Towards Building Comprehensive AI

ABSTRACTThe article presents major principles of building complex hybrid systems for knowledge engineering. The systems make use of neural networks (for low level processing), fuzzy systems (for intermediate level processing), and symbolic AI systems (for higher level processing). Knowledge acquisition and rules extraction is considered an important part of the whole system. An experimental environment, Fuzzy COPE, which facilitates building comprehensive AI systems, is described. It consists of data a analysis module, a neural network module, a fuzzy inference module, a production rules module, and a fuzzy rules extraction module. Such an environment makes possible using all of the three paradigms, i.e. fuzzy rules, neural networks and symbolic production rules, as well as other paradigms of soft computing, in one system. A methodology for building comprehensive AI systems is described. The use of Fuzzy COPE for building hybrid systems is illustrated on a test problem for decision making in stock trading...

Management of uncertainty in LIFE FEShell fuzzy frame system

LIFE FEShell is a fuzzy expert system shell that handles several kinds of uncertainty. LIFE FEShell is based on fuzzy logic, possibility theory, fuzzy measures and the fuzzy integral and is constructed in four parts: Fuzzy Production System (FPS), Fuzzy Frame System (FFS), FPS Object Editor (FPOE), and Fuzzy Frame Editor (FFE). We are now developing a few expert systems on LIFE FEShell to study and solve problems that are likely to arise during the implementation of these systems. In this paper, we present an analysis of uncertainty in the frame system and show details of the LIFE FEShell Fuzzy Frame System and its handling of uncertainty. LIFE FEShell FFS allows linguistic representations in data and evaluation functions and handles its hierarchical definitions to accommodate their linguistic polysemy. Data may be set not only with crisp values, but also using possibility distributions as slot values, data grades, and link grades.

ファジィプロダクションシステムによる移動ロボットの障害物回避問題の検討

We report a method for obstacle avoidance control against moving or standing obstacles for mobile robot. The speed of moving obstacles is uncertain, and their speed is expressed using fuzzy numbers. This paper presents an obstacle avoidance algorithm using fuzzy production system. Some results of computer simulation are shown to evaluate the effectiveness of the proposed method.

Implementing fuzzy rule-based systems on silicon chips

The authors address the implementation of a fuzzy simulator (FSIM) and discuss architectures for a general-purpose VLSI fuzzy-inference processor. The FSIM tool aids in the rapid prototyping of fuzzy production system (FPS), and represents a convenient transitional step in the implementation of an FPS on silicon. They present a brief theoretical review of fuzzy reasoning, introduce the FSIM, and discuss FPS development using the FSIM. An overall picture of the various stages involved in developing a fuzzy-inference processor is provided. The authors then outline the general architecture of a VLSI inference processor for FPSs. To further illustrate the development of a fuzzy inference processor, they describe an FPS example from conceptualization to implementation on silicon chips. >

Managing uncertainty in a fuzzy expert system

All data and rules in a fuzzy expert system are accompanied by their degree of confidence values. This paper is concerned with processing these confidence values during one round of rule firing in a fuzzy expert system. Part I discusses determining the final confidence in the left hand side of a rule which includes: (1) pattern evaluation; (2) finding the confidence in the antecedent; and (3) combining rule and antecendent confidence. Part II discusses the maintenance of memory in a fuzzy expert production system, in both deductive reasoning systems (with sequential rule-firing schemes) and in inductive reasoning systems (with rules fired in parallel).

A parallel rule firing fuzzy production system with resolution of memory conflicts by weak fuzzy monotonicity, applied to the classification of multiple objects characterized by multiple uncertain features

A fuzzy production system shell is described characterized by parallel rather than sequential rule firing. All fireable rules are fired in effect concurrently. Since there is no unfired-rule stack, no backtracking can take place, and no rule conflict algorithm is necessary; instead, a memory conflict algorithm is invoked when more than one rule modifies the same datum. Memory conflicts are resolved by weakly monotonie fuzzy logic; i.e. the value or truth value of an attribute may be replaced if the new truth value is equal to or greater than the old truth value. The system depends heavily on the use of fuzzy logic and on confidence levels, fuzzy numbers and fuzzy sets as explicit data types, and on the generation of rules from a data base of expert knowledge. Fuzzy sets are used to store contradictory and ambiguous information and results. If a problem is suitable for parallel processing, substantial reductions in system overhead are achieved, together with substantial economy in the number of rules which must be written; if a problem is not suitable for parallel processing, no economy is achieved. We suggest that problems which yield to deductive reasoning constitute a class which is suitable for sequential rule firing, and problems which yield to inductive reasoning constitute a class suitable for parallel processing. A successful application of the system to the unsupervised analysis of a time sequence of noisy echocardiogram images is described.

Generalized network modeling and diagnosis using financial ratios

In Section 1 of this paper, we briefly review the general problem of diagnosing a fault in a network of interacting components. The fuzzy form of this general problem, in which component failure is not all-or-none, is introduced; many important practical diagnosis problems resemble the fuzzy case more closely than the crisp case. Section 2 discusses the use of a fuzzy production system as a powerful technique for performing an automated preliminary diagnosis in a fuzzy network. fault, a fuzzy production system to analyze the financial performance of a business firm using linguistic assessments of financial indicator ratios, is presented in Section 3 as an example of fuzzy network diagnosis. Section 4 presents some recent work in automatic program generation for fuzzy network diagnosis, and Section 5 briefly mentions some work currently in progress.